Dynamic vessel adaptation in synthetic arteriovenous networks

Area of Science:

• Physiology
• Bioengineering
• Computational Biology

Background:

• Blood vessel networks exhibit continuous structural adaptation driven by hemodynamic and metabolic stimuli.
• Vessel morphology critically influences blood flow characteristics within a fixed network arrangement.
• Vessel diameters dynamically adjust in response to stimuli like shear stress, pressure, and electrical signals.

Purpose of the Study:

• To apply a theoretical vessel adaptation model to synthetic arteriovenous networks generated by the "Tumorcode" framework.
• To investigate how local stimuli influence long-term changes in vessel diameters.
• To compare network characteristics derived from the adaptation algorithm with those predicted by Murray's Law.

Main Methods:

• Utilized a simulation framework called "Tumorcode" to generate synthetic arteriovenous blood vessel networks.
• Applied a theoretical vessel adaptation algorithm incorporating four local stimuli: wall shear stress, intravascular pressure, metabolic stimuli, and electrical stimuli.
• Optimized free model parameters using a method that ensured homogeneous flow within the capillary bed.

Main Results:

• The study found that vessel radii obtained through the adaptation algorithm closely approximate Murray's Law.
• Significant differences were observed in local blood volume, surface-to-volume ratio, and branching ratios between networks adhering strictly to Murray's Law and those generated by the adaptation algorithm.
• The adaptation algorithm successfully simulated long-term changes in vessel diameters within microvascular networks.

Conclusions:

• The findings suggest that while vessel adaptation is closely related to Murray's Law, it leads to distinct network properties.
• The "Tumorcode" framework and the applied adaptation algorithm provide a valuable tool for studying microvascular network remodeling.
• Understanding these adaptations is crucial for comprehending vascular health and disease, particularly in complex networks like those found in tumors.